{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing French train data\n",
    "     \n",
    "## Background\n",
    "The SNCF (National Society of French Railways) is France's national state-owned railway company. Founded in 1938, it operates the country's national rail traffic along with Monaco, including the TGV, France's high-speed rail network. This dataset covers 2015-2018 with many different train stations. The dataset primarily covers aggregate trip times, delay times, cause for delay, etc., for each station there are 27 columns in total. A TGV route map can be seen [here](https://en.wikipedia.org/wiki/TGV#/media/File:France_TGV.png).\n",
    "\n",
    "## The data\n",
    "The source data set is available from the [SNCF](https://ressources.data.sncf.com/explore/dataset/regularite-mensuelle-tgv-aqst/information/). Check out this [visualization](https://twitter.com/noccaea/status/1095735292206739456) of it. This has been used in a [tidy tuesday](https://github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-02-26) previously. The [full data set](https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/full_trains.csv) is available but we will work with a [subset](https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/small_trains.csv)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# allow plots to appear directly in the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Call chaining example\n",
    "First, we run all the methods using pyjanitor's preferred call chaining approach. This code updates the column names, removes any empty rows/columns, and drops some unneeded columns in a very readable manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>departure_station</th>\n",
       "      <th>arrival_station</th>\n",
       "      <th>journey_time_avg</th>\n",
       "      <th>total_num_trips</th>\n",
       "      <th>avg_delay_all_departing</th>\n",
       "      <th>avg_delay_all_arriving</th>\n",
       "      <th>num_departing_late</th>\n",
       "      <th>num_arriving_late</th>\n",
       "      <th>prop_late_departures</th>\n",
       "      <th>prop_late_arrivals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>PARIS EST</td>\n",
       "      <td>METZ</td>\n",
       "      <td>85.133779</td>\n",
       "      <td>299</td>\n",
       "      <td>0.752007</td>\n",
       "      <td>0.419844</td>\n",
       "      <td>15</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.050167</td>\n",
       "      <td>0.056856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>REIMS</td>\n",
       "      <td>PARIS EST</td>\n",
       "      <td>47.064516</td>\n",
       "      <td>218</td>\n",
       "      <td>1.263518</td>\n",
       "      <td>1.137558</td>\n",
       "      <td>10</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.045872</td>\n",
       "      <td>0.105505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>PARIS EST</td>\n",
       "      <td>STRASBOURG</td>\n",
       "      <td>116.234940</td>\n",
       "      <td>333</td>\n",
       "      <td>1.139257</td>\n",
       "      <td>1.586396</td>\n",
       "      <td>20</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.060060</td>\n",
       "      <td>0.057057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month departure_station arrival_station  journey_time_avg  \\\n",
       "0  2017      9         PARIS EST            METZ         85.133779   \n",
       "1  2017      9             REIMS       PARIS EST         47.064516   \n",
       "2  2017      9         PARIS EST      STRASBOURG        116.234940   \n",
       "\n",
       "   total_num_trips  avg_delay_all_departing  avg_delay_all_arriving  \\\n",
       "0              299                 0.752007                0.419844   \n",
       "1              218                 1.263518                1.137558   \n",
       "2              333                 1.139257                1.586396   \n",
       "\n",
       "   num_departing_late  num_arriving_late  prop_late_departures  \\\n",
       "0                  15               17.0              0.050167   \n",
       "1                  10               23.0              0.045872   \n",
       "2                  20               19.0              0.060060   \n",
       "\n",
       "   prop_late_arrivals  \n",
       "0            0.056856  \n",
       "1            0.105505  \n",
       "2            0.057057  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chained_df = (\n",
    "    # ingest raw data\n",
    "    pd.read_csv(\n",
    "        \"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/small_trains.csv\"\n",
    "    )\n",
    "    # removes whitespace, punctuation/symbols, capitalization\n",
    "    .clean_names()\n",
    "    # removes entirely empty rows / columns\n",
    "    .remove_empty()\n",
    "    # renames 1 column\n",
    "    .rename_column(\"num_late_at_departure\", \"num_departing_late\")\n",
    "    # drops 3 unnecessary columns\n",
    "    .drop(columns=[\"service\", \"delay_cause\", \"delayed_number\"])\n",
    "    # add 2 new columns with a calculation\n",
    "    .join_apply(\n",
    "        lambda df: df.num_departing_late / df.total_num_trips, \"prop_late_departures\"\n",
    "    )\n",
    "    .join_apply(\n",
    "        lambda df: df.num_arriving_late / df.total_num_trips, \"prop_late_arrivals\"\n",
    "    )\n",
    ")\n",
    "\n",
    "chained_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step by step through the methods\n",
    "Now, we will import the French data again and then use the methods from the call chain one at a time. Our subset of the train data has over 32000 rows and 13 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32772, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    \"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/small_trains.csv\"\n",
    ")\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning column names\n",
    "The clean_names method converts the column names to lowercase and replaces all spaces with underscores. For this data set, it actually does not modify any of the names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_columns = df.columns\n",
    "df = df.clean_names()\n",
    "new_columns = df.columns\n",
    "original_columns == new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'month', 'service', 'departure_station', 'arrival_station',\n",
       "       'journey_time_avg', 'total_num_trips', 'avg_delay_all_departing',\n",
       "       'avg_delay_all_arriving', 'num_late_at_departure', 'num_arriving_late',\n",
       "       'delay_cause', 'delayed_number'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming columns\n",
    "We rename the \"num_late_at_departure\" column for consistency purposes with the rename_column method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'month', 'service', 'departure_station', 'arrival_station',\n",
       "       'journey_time_avg', 'total_num_trips', 'avg_delay_all_departing',\n",
       "       'avg_delay_all_arriving', 'num_departing_late', 'num_arriving_late',\n",
       "       'delay_cause', 'delayed_number'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename_column(\"num_late_at_departure\", \"num_departing_late\")\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping empty columns and rows\n",
    "The remove_empty method looks for empty columns and rows and drops them if found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32772, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32772, 13)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.remove_empty()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop unneeded columns\n",
    "We identify 3 columns that we decided are unnecessary for the analysis and can quickly drop them with the aptly named drop_columns method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=[\"service\", \"delay_cause\", \"delayed_number\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'month', 'departure_station', 'arrival_station',\n",
       "       'journey_time_avg', 'total_num_trips', 'avg_delay_all_departing',\n",
       "       'avg_delay_all_arriving', 'num_departing_late', 'num_arriving_late'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('PARIS LYON', 6834),\n",
       " ('PARIS MONTPARNASSE', 4512),\n",
       " ('PARIS EST', 1692),\n",
       " ('LYON PART DIEU', 1476),\n",
       " ('PARIS NORD', 1128),\n",
       " ('MARSEILLE ST CHARLES', 1044),\n",
       " ('LILLE', 846),\n",
       " ('RENNES', 630),\n",
       " ('NANTES', 630),\n",
       " ('MONTPELLIER', 564)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gives us the top ten departure stations from that column\n",
    "Counter(df[\"departure_station\"]).most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use seaborn to quickly visualize how quickly departure and arrivals times were late versus the total number of trips for each of the over 30000 routes in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f3733c9bad0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 705.6x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(\n",
    "    df,\n",
    "    x_vars=[\"num_departing_late\", \"num_arriving_late\"],\n",
    "    y_vars=\"total_num_trips\",\n",
    "    height=7,\n",
    "    aspect=0.7,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add additional statistics as new columns\n",
    "We can add columns containing additional statistics concerning the proportion of time each route is late either departing or arriving by using the add_columns method for each route. \n",
    "\n",
    "Note the difference between how we added the two columns below and the same code in the chained_df file creation at the top of the notebook. In order to operate on the df that was in the process of being created in the call chain, we had to use join_apply with a lambda function instead of the add_columns method. Alternatively, we could have split the chain into two separate chains with the df being created in the first chain and the add_columns method being used in the second chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>departure_station</th>\n",
       "      <th>arrival_station</th>\n",
       "      <th>journey_time_avg</th>\n",
       "      <th>total_num_trips</th>\n",
       "      <th>avg_delay_all_departing</th>\n",
       "      <th>avg_delay_all_arriving</th>\n",
       "      <th>num_departing_late</th>\n",
       "      <th>num_arriving_late</th>\n",
       "      <th>prop_late_departures</th>\n",
       "      <th>prop_late_arrivals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>PARIS EST</td>\n",
       "      <td>METZ</td>\n",
       "      <td>85.133779</td>\n",
       "      <td>299</td>\n",
       "      <td>0.752007</td>\n",
       "      <td>0.419844</td>\n",
       "      <td>15</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.050167</td>\n",
       "      <td>0.056856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>REIMS</td>\n",
       "      <td>PARIS EST</td>\n",
       "      <td>47.064516</td>\n",
       "      <td>218</td>\n",
       "      <td>1.263518</td>\n",
       "      <td>1.137558</td>\n",
       "      <td>10</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.045872</td>\n",
       "      <td>0.105505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>PARIS EST</td>\n",
       "      <td>STRASBOURG</td>\n",
       "      <td>116.234940</td>\n",
       "      <td>333</td>\n",
       "      <td>1.139257</td>\n",
       "      <td>1.586396</td>\n",
       "      <td>20</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.060060</td>\n",
       "      <td>0.057057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month departure_station arrival_station  journey_time_avg  \\\n",
       "0  2017      9         PARIS EST            METZ         85.133779   \n",
       "1  2017      9             REIMS       PARIS EST         47.064516   \n",
       "2  2017      9         PARIS EST      STRASBOURG        116.234940   \n",
       "\n",
       "   total_num_trips  avg_delay_all_departing  avg_delay_all_arriving  \\\n",
       "0              299                 0.752007                0.419844   \n",
       "1              218                 1.263518                1.137558   \n",
       "2              333                 1.139257                1.586396   \n",
       "\n",
       "   num_departing_late  num_arriving_late  prop_late_departures  \\\n",
       "0                  15               17.0              0.050167   \n",
       "1                  10               23.0              0.045872   \n",
       "2                  20               19.0              0.060060   \n",
       "\n",
       "   prop_late_arrivals  \n",
       "0            0.056856  \n",
       "1            0.105505  \n",
       "2            0.057057  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prop = df.add_columns(\n",
    "    prop_late_departures=df.num_departing_late / df.total_num_trips,\n",
    "    prop_late_arrivals=df.num_arriving_late / df.total_num_trips,\n",
    ")\n",
    "\n",
    "df_prop.head(3)"
   ]
  }
 ],
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